Punctuality statistics provide valuable insights into the reliability and performance of flights and airlines. Travelers can use this information to make informed decisions about their travel plans, and airlines can use it to improve their operations and enhance the passenger experience.
Goal: To investigate 2018 Jan flight punctuality statistic and provide analysis, insights and visualisation.
Punctuality statistics of a flight provide valuable information about the on-time performance of a specific flight or an airline. Here are some key insights that can be derived from punctuality statistics:
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On-Time Performance: Punctuality statistics indicate how often a flight or airline arrives at its destination on time, which is typically defined as being within a certain number of minutes of the scheduled arrival time (e.g., within 15 minutes of the scheduled arrival time). This information is crucial for passengers who want to plan their travel schedules efficiently.
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Delays: Punctuality statistics reveal the frequency and duration of delays for a flight or airline. Delays can occur due to various reasons, such as weather conditions, air traffic congestion, mechanical issues, or operational challenges. Understanding the extent of delays helps passengers assess the reliability of a particular flight or airline.
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Flight Cancellations: Punctuality statistics may also include information about flight cancellations. Cancellations can disrupt travel plans significantly, so knowing the cancellation rate of a flight or airline is essential for passengers.
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Comparison Between Airlines: Passengers often use punctuality statistics to compare the performance of different airlines when selecting flights. Airlines with a strong track record of on-time arrivals may be more attractive to travelers.
These metrics are important for airlines and passengers to track as they provide insights into the operational reliability and performance of an airline, as well as potential disruptions to travelers' plans. Airlines aim to maximize the number of flights matched while minimizing the number of flights canceled and actual flights unmatched to provide a smooth and reliable travel experience for passengers.Travelers can use this information to make informed decisions about their travel plans, and airlines can use it to improve their operations and enhance the passenger experience.
There are 3207 rows, 25 columns. There are Column types of both categorical and numerical and they provide us the information about the flight details such as their run date, reporting period, reporting airport, origin destination country, origin destination, airline name, schedule or charter, number flights matched, actual flights unmatched, number flights canceled, flights punctuality percentages, average delay mins, and their previous record
The below checklist is done for data cleaning using (google sheet):
Ans: There are no duplicate rows
Ans: There are no missing values
Ans: Change run_date to date time data type, others no issue
Ans: No irrelevant column
Ans: No inconsistent data entry
Ans: Trimming some white spaces using google clean-up suggestion
Ans: no wrong spelling
Ans: no numerical errors
Cleaned Datasets:https://docs.google.com/spreadsheets/d/1OHK0vSzopM_YVklA4jY7IJBB2aoWP2rZ5SEGahc22Pk/edit?usp=sharing
Different data analysis strategies are used to analyse the dataset provided:
Helps describe the data, and generate insight from the characteristic of the data. A customer might want to look into the performance in terms of punctuality based on different flight lines, punctuality and data should aggregate based on flight lines.
From the above shape and size table of the dataset we can see general max and min of the data base on different categories.
From the above two bar charts we can see the frequency of airports and airlines for 2018 Jan
Summarized the large datasets into insightful numbers and gist of information about the data, We can understand the general situation, make decisions and monitor the changes.
Mean (Average of a data set), Median (middle value of the data set), Mode (most repeated number)
Ans: The overall flight as a delay of 190mins with a median of 22mins.
Mode (Most occurrance) is no delay (0 mins) with 39 counts.
To understand the spread and distribution of data. and to find outliers.
Ans: base on the interquartile, there are 29 airline outliers which has delay time more than 182 minutes (MAX IQR).
Interquartile candlestick chart:
From the historgram, most of the flight airlines able to comply with no delay. We can also notice on some extreme outliers from the historgram with high positive skew.
Ans: Dash board
A data story is created through Tableau using this dataset: https://public.tableau.com/shared/FWJS5T4CK?:display_count=n&:origin=viz_share_link
The first page shows the overview flight punctuality statistic for 2018 Jan
The 2nd page shows the punctuality performance for each airlines, in the order from worst to best.
The last page shows the summary of the flight delay for each airline and the performance comparison between Jan 2018 and Jan 2017.
From the punctuality analysis and visualisation, there are a lot of airlines that comply with zero delay as well as many airlines that has extreme cases of delay. From the Visualisation we can also notice that airlines with many flight frequency can also caused much delay. This might be due to overpressure and overwork of the stuff align with tight schedule. Overall 2018 Jan performance is better than 2017 Jan punctuality performance with the average delay time of 13.29 mins reduce to 11.57 mins.
Recommendations: Traverlers should plan ahead and do a comparison study to avoid missing interconnecting flight due to long flight delay. Flight airline should also reduce their flight frequnecy to prevent overwork from the staff and delay of flgiht to preserve their reputation.